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InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation

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シラバス - 本コースの学習内容

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Inference Overview

This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference).

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2件のビデオ (合計25分)
2件のビデオ
Overview: MAP Inference9 分
1時間で修了

Variable Elimination

This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure.

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4件のビデオ (合計56分), 1 quiz
4件のビデオ
Complexity of Variable Elimination12 分
Graph-Based Perspective on Variable Elimination15 分
Finding Elimination Orderings11 分
1の練習問題
Variable Elimination18 分
2
18時間で修了

Belief Propagation Algorithms

This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties.

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9件のビデオ (合計150分), 3 quizzes
9件のビデオ
Properties of Cluster Graphs15 分
Properties of Belief Propagation9 分
Clique Tree Algorithm - Correctness18 分
Clique Tree Algorithm - Computation16 分
Clique Trees and Independence15 分
Clique Trees and VE16 分
BP In Practice15 分
Loopy BP and Message Decoding21 分
2の練習問題
Message Passing in Cluster Graphs10 分
Clique Tree Algorithm10 分
3
1時間で修了

MAP Algorithms

This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task.

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5件のビデオ (合計74分), 1 quiz
5件のビデオ
Finding a MAP Assignment3 分
Tractable MAP Problems15 分
Dual Decomposition - Intuition17 分
Dual Decomposition - Algorithm16 分
1の練習問題
MAP Message Passing4 分
4
14時間で修了

Sampling Methods

In this module, we discuss a class of algorithms that uses random sampling to provide approximate answers to conditional probability queries. Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple Gibbs sampling algorithm, as well as a family of methods known as Metropolis-Hastings.

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5件のビデオ (合計100分), 3 quizzes
5件のビデオ
Markov Chain Monte Carlo14 分
Using a Markov Chain15 分
Gibbs Sampling19 分
Metropolis Hastings Algorithm27 分
2の練習問題
Sampling Methods14 分
Sampling Methods PA Quiz8 分
26分で修了

Inference in Temporal Models

In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks.

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1件のビデオ (合計20分), 1 quiz
1件のビデオ
1の練習問題
Inference in Temporal Models6 分
4.6
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Probabilistic Graphical Models 2: Inference からの人気レビュー

by LLMar 12th 2017

Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.

by YPMay 29th 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

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Daphne Koller

Professor
School of Engineering

スタンフォード大学(Stanford University)について

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States....

Probabilistic Graphical Models の専門講座について

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems....
Probabilistic Graphical Models

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  • Execute the basic steps of a variable elimination or message passing algorithm

    Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible

    Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings

    Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective

    Design Metropolis Hastings proposal distributions that are more likely to give good results

    Compute a MAP assignment by exact inference

    Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

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